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Change Management Part 1: The Big Picture

Dennis Drogseth

This is the first of a three-part series on change management. In this blog, I’ll try to answer the question, “What is change management?” from both a process and a benefits (or use-case) perspective.

In the second installment, I’ll address best practices for both planning for and measuring the success of change management initiatives. I’ll also examine some of the issues that EMA has seen arise when IT organizations try to establish a more cohesive cross-domain approach to managing change. In part three, I’ll focus on the impacts of cloud, agile, and mobile, including the growing need for investments in automation and analytics to make change management more effective.

Change Management Processes

Like many words and concepts in English language, especially when applied to technology, “change management” carries with it a wide variety of associations. In terms of the processes established in the IT Infrastructure Library (ITIL), change management is best understood as a strategic approach to planning for change.

ITIL defines change management succinctly as, “the process responsible for controlling the lifecycle of all changes, enabling beneficial changes to be made with minimum disruption to IT Services.” As such, change management is a logical system of governance that addresses a set of relevant questions, which include the following:

■ Who requested the change?

■ What is the reason for the change?

■ What is the desired result of the change?

■ What are the risks involved with making the change?

■ What resources are required to deliver the change?

■ Who is responsible for the build, test, and implementation of the change?

■ What is the relationship between this change and other changes?

But this system of governance doesn’t stand alone. Actually implementing and managing changes requires attention to other ITIL processes. These include (but are not limited to):

■ Service asset and configuration management (SACM) – “The process responsible for maintaining information about configuration items required to deliver an IT Service, including their relationships.” SACM addresses how IT hardware and software assets (including applications) have been configured and, even more critically, identifies the relationships and interdependencies affecting infrastructure and application assets.

■ Release and deployment management – “The process responsible for planning, scheduling and controlling the build, test and deployment of releases, and for delivering new functionality required by the business while protecting the integrity of existing services.” As you can imagine, release management and automation should go hand in hand.

There are other ITIL processes relevant to managing change effectively, including capacity management, problem management, availability management, and continual service improvement, just to name a few. From just this brief snapshot, you might get the (correct) impression that change management in the “big picture” is at the very heart of effective IT operations. If done correctly, change management touches all of IT—including the service desk, operational teams, development, the executive suite, and even non-IT service consumers. This central position makes change management both an opportunity and a challenge.

Change Management Use Cases

Image removed.Probably the best way to understand the “change management opportunity” is to look at some of the use cases affiliated with it. Effective change management can empower a wide range of other initiatives, from lifecycle asset management to DevOps, service impact management, and improved service performance. EMA consultants have estimated that more than 60% of IT service disruptions come from the impacts of changes made across the application infrastructure—and this estimate is conservative compared to some of the other industry estimates I’ve seen. Having good change management processes and technologies in place is also a foundation for better automation, as well as for better optimization of both public and private cloud resources. And the list goes on.

Even the list below, derived in large part from CMDB Systems: Making Change Work in the Age of Cloud and Agile, is a partial one, but it should provide a useful departure point for your planning—as you seek to prioritize the use case(s) most relevant to you.

■ Governance and compliance: Managing change to conform with critical industry, security, and asset-related requirements for compliance, while minimizing change-related disruptions. This, can provide significant financial benefits including OpEx savings, superior service availability, improved security and savings from avoiding the penalty costs incurred when changes are made poorly.

■ Data center consolidation—mergers and acquisitions: Planning new options for data center consolidation is definitely on the rise, and mergers and acquisitions often lead to data center consolidation initiatives. Effective change management can shorten consolidation time, minimize costs, and improve the quality of the outcome.

■ Disaster recovery – Disaster recovery initiatives may be an extension of data center consolidation, or they may be independent. Automating change for disaster recovery is one of the more common drivers for a more systemic approach to change management.

■ The proverbial “move to cloud” – The stunning rise of virtualization and the persistent move to assimilate both internal and public cloud options make change impact management and effective change automation essential.

■ Facilities management and Green IT – This use case requires dynamic insights into both configuration and “performance”-related attributes for configuration items (CIs), both internal to IT (servers, switches, desktops, etc.) and external to traditional IT boundaries (facilities, power, etc.).

■ Optimizing the end-user experience across heterogeneous endpoints – Meeting the challenges of unified endpoint management including mobile endpoints, requires a flexible adoption of change management best practices and automation. But the benefits of doing this can be significant—impacting asset management, security, and financial optimization, while increasing end-user satisfaction with IT services.

Change Management Part 2

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

Change Management Part 1: The Big Picture

Dennis Drogseth

This is the first of a three-part series on change management. In this blog, I’ll try to answer the question, “What is change management?” from both a process and a benefits (or use-case) perspective.

In the second installment, I’ll address best practices for both planning for and measuring the success of change management initiatives. I’ll also examine some of the issues that EMA has seen arise when IT organizations try to establish a more cohesive cross-domain approach to managing change. In part three, I’ll focus on the impacts of cloud, agile, and mobile, including the growing need for investments in automation and analytics to make change management more effective.

Change Management Processes

Like many words and concepts in English language, especially when applied to technology, “change management” carries with it a wide variety of associations. In terms of the processes established in the IT Infrastructure Library (ITIL), change management is best understood as a strategic approach to planning for change.

ITIL defines change management succinctly as, “the process responsible for controlling the lifecycle of all changes, enabling beneficial changes to be made with minimum disruption to IT Services.” As such, change management is a logical system of governance that addresses a set of relevant questions, which include the following:

■ Who requested the change?

■ What is the reason for the change?

■ What is the desired result of the change?

■ What are the risks involved with making the change?

■ What resources are required to deliver the change?

■ Who is responsible for the build, test, and implementation of the change?

■ What is the relationship between this change and other changes?

But this system of governance doesn’t stand alone. Actually implementing and managing changes requires attention to other ITIL processes. These include (but are not limited to):

■ Service asset and configuration management (SACM) – “The process responsible for maintaining information about configuration items required to deliver an IT Service, including their relationships.” SACM addresses how IT hardware and software assets (including applications) have been configured and, even more critically, identifies the relationships and interdependencies affecting infrastructure and application assets.

■ Release and deployment management – “The process responsible for planning, scheduling and controlling the build, test and deployment of releases, and for delivering new functionality required by the business while protecting the integrity of existing services.” As you can imagine, release management and automation should go hand in hand.

There are other ITIL processes relevant to managing change effectively, including capacity management, problem management, availability management, and continual service improvement, just to name a few. From just this brief snapshot, you might get the (correct) impression that change management in the “big picture” is at the very heart of effective IT operations. If done correctly, change management touches all of IT—including the service desk, operational teams, development, the executive suite, and even non-IT service consumers. This central position makes change management both an opportunity and a challenge.

Change Management Use Cases

Image removed.Probably the best way to understand the “change management opportunity” is to look at some of the use cases affiliated with it. Effective change management can empower a wide range of other initiatives, from lifecycle asset management to DevOps, service impact management, and improved service performance. EMA consultants have estimated that more than 60% of IT service disruptions come from the impacts of changes made across the application infrastructure—and this estimate is conservative compared to some of the other industry estimates I’ve seen. Having good change management processes and technologies in place is also a foundation for better automation, as well as for better optimization of both public and private cloud resources. And the list goes on.

Even the list below, derived in large part from CMDB Systems: Making Change Work in the Age of Cloud and Agile, is a partial one, but it should provide a useful departure point for your planning—as you seek to prioritize the use case(s) most relevant to you.

■ Governance and compliance: Managing change to conform with critical industry, security, and asset-related requirements for compliance, while minimizing change-related disruptions. This, can provide significant financial benefits including OpEx savings, superior service availability, improved security and savings from avoiding the penalty costs incurred when changes are made poorly.

■ Data center consolidation—mergers and acquisitions: Planning new options for data center consolidation is definitely on the rise, and mergers and acquisitions often lead to data center consolidation initiatives. Effective change management can shorten consolidation time, minimize costs, and improve the quality of the outcome.

■ Disaster recovery – Disaster recovery initiatives may be an extension of data center consolidation, or they may be independent. Automating change for disaster recovery is one of the more common drivers for a more systemic approach to change management.

■ The proverbial “move to cloud” – The stunning rise of virtualization and the persistent move to assimilate both internal and public cloud options make change impact management and effective change automation essential.

■ Facilities management and Green IT – This use case requires dynamic insights into both configuration and “performance”-related attributes for configuration items (CIs), both internal to IT (servers, switches, desktops, etc.) and external to traditional IT boundaries (facilities, power, etc.).

■ Optimizing the end-user experience across heterogeneous endpoints – Meeting the challenges of unified endpoint management including mobile endpoints, requires a flexible adoption of change management best practices and automation. But the benefits of doing this can be significant—impacting asset management, security, and financial optimization, while increasing end-user satisfaction with IT services.

Change Management Part 2

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...